It is highly desirable to reliably track respiration within patients having pacemakers and ICDs. Tracking patient respiration permits potentially dangerous respiratory disorders, such as apnea, hypopnea, hyperpnea, nocturnal asthma, and Cheyne-Stokes Respiration (CSR), to be detected. Apnea and hypopnea are abnormal respiration patterns characterized by periods of significantly reduced respiration. With hypopnea, respiration is reduced but still present. With apnea, however, respiration may cease completely for 10 seconds or longer. One common form of apnea is sleep apnea, in which hundreds of individual episodes of apnea can occur during a single night. Accordingly, patients with sleep apnea experience frequent wakefulness at night and excessive sleepiness during the day. In addition, apnea can exacerbate various medical conditions, particularly congestive heart failure (CHF) wherein the patient suffers from poor cardiac function. Indeed, the aberrant blood chemistry levels occurring during sleep apnea are a significant problem for patients with CHF. Due to poor cardiac function caused by CHF, patients already suffer from generally low blood oxygen levels. Frequent periods of sleep apnea result in even lower blood oxygen levels.
Episodes of apnea can also occur during Cheyne-Stokes Respiration (CSR), which is an abnormal respiratory pattern often occurring in patients with CHF. CSR is characterized by alternating periods of hypopnea and hyperpnea (i.e. fast, deep breathing.) Briefly, CSR arises principally due to a time lag between blood CO2 levels sensed by the respiratory control nerve centers of the brain and the blood CO2 levels. With CHF, poor cardiac function results in poor blood flow to the brain such that respiratory control nerve centers respond to blood CO2 levels that are no longer properly representative of the overall blood CO2 levels in the body. Hence, the respiratory control nerve centers trigger an increase in the depth and frequency of breathing in an attempt to compensate for perceived high blood CO2 levels—although the blood CO2 levels have already dropped. By the time the respiratory control nerve centers detect the drop in blood CO2 levels and act to slow respiration, the blood CO2 levels have already increased. This cycle becomes increasingly unbalanced until respiration alternates between hypopnea and hyperpnea. The periods of hypopnea often become sufficiently severe that no breathing occurs between the periods of hyperpnea, i.e. periods of frank apnea occur between the periods of hyperpnea. The wildly fluctuating blood chemistry levels caused by alternating between hyperpnea and apnea/hypopnea can significantly exacerbate CHF and other medical conditions. When CHF is still mild, CSR usually occurs, if at all, only while the patient is sleeping. When it becomes more severe, CSR can occur while the patient is awake.
Abnormal respiration during sleep may also arise due to nocturnal asthma. With asthma, the linings of the airways swell and become more inflamed. Mucus clogs the airways and the muscles around the airways tighten and narrow. Hence, breathing becomes difficult and stressful. During an asthma attack, rapid breathing patterns similar to hyperpnea occur, though little or no oxygen actual reaches the lungs. An asthma attack may be triggered by allergens, respiratory infections, cold and dry air, or even heartburn. The majority of asthma attacks occur during the night, between 3:00 a.m. and 5:00 a.m. Nocturnal asthma has been associated with factors such as decreased pulmonary function, hypoxemia and circadian variations of histamine, epinephrine, and cortisol concentrations. Asthma attacks at night may also be triggered directly by sleep apnea. Nocturnal asthma attacks may be fatal, particularly within patients also suffering from CHF.
In view of the significant adverse consequences of apnea/hypopnea, nocturnal asthma, or CSR, particularly insofar as patients with CHF are concerned, it is highly desirable to provide techniques for detecting such conditions. Tracking actual patient respiration provides perhaps the most direct and effective technique for detecting respiratory disorders. For patients with pacemakers and ICDs, respiration is conventionally tracked based on thoracic impedance as measured via pacing/sensing leads implanted within the heart. Sensing of the intracardiac electrogram (IEGM) of the patient is temporarily suspended during each cardiac cycle so as to sense an impedance signal, from which respiration patterns are derived. See, for example, U.S. Pat. No. 6,449,509 to Park et al., entitled “Implantable Stimulation Device Having Synchronous Sampling for a Respiration Sensor.”
Although impedance-based techniques are useful, it would be desirable to provide alternative techniques for tracking respiration, particularly for the purposes of detecting episodes of abnormal respiration, wherein respiration is derived solely from the IEGM signal so as to eliminate the need to detect or process impedance. Additionally, this eliminates need for additional sensors, and the sensing electrodes can be thus used for IEGM based breathing pattern detection and hence, the ease of implementability in current platforms. One technique for deriving respiration from an IEGM signal is set forth in U.S. Pat. No. 6,697,672 to Andersson, entitled “Implantable Heart Stimulator.” Briefly, Andersson provides a technique to extract parameters related to patient respiration from an analysis of intervals between various events detected within a ventricular-IEGM (i.e. V-IEGM) signal. For example, cycle-to-cycle variability is tracked in R-R intervals or in the amplitude of S-T intervals. In other words, the technique of Andersson exploits changes in the durations of intervals within the V-IEGM to track respiration. Although not discussed in the Andersson reference, it is believed that autonomic variability arising during respiration causes the changes in intervals. R-waves (also referred to as QRS-complexes) are electrical signals representative of the depolarization of ventricular muscle tissue. The subsequent electrical repolarization of the ventricular tissue appears within the IEGM as a T-wave. Electrical depolarization of atrial muscle tissue is manifest as a P-wave. Strictly speaking, P-waves, R-waves and T-waves are features of a surface electrocardiogram (EKG or ECG). For convenience, the terms P-wave, R-wave and T-wave are also used herein (and in the literature) to refer to the corresponding internal signal component.
Another technique is set forth in U.S. Patent Application 60/631,111, of Bharmi et al., entitled “System and Method for Detection of Respiration Patterns via Intracardiac Electrogram Signals,” the disclosure of which is incorporated herein by reference. With the technique Bharmi et al., respiration patterns are detected based upon cycle-to-cycle changes in morphological features associated with individual electrical events with the IEGM signals. In one example, IEGM signals are sensed and individual cardiac cycles are identified therein. Selected individual electrical events (such as P-waves, QRS-complexes or T-waves) are identified within the cardiac cycles and one or more parameters associated with the individual features are detected (such as maximum amplitude, peak-to-peak amplitude, or numerical integral). Then, patient respiration is tracked based on cycle-to-cycle changes in the detected parameters associated with the individual selected electrical events. Hence, in contrast to the technique of Andersson, which primarily relates to changes in the duration of intervals, the technique of Bharmi et al. instead examines changes within the shape of individual features of the IEGM such as P-waves, QRS-complexes or T-waves. It is believed that that slight displacement of IEGM sensing electrodes caused by movement of the thorax during respiration causes slight variations in the size and shape of individual electrical events of the IEGM signals, such as P-waves, and that those changes are correlated with respiration. This differs from changes in the durations of intervals (such as R-R intervals), which, as noted, appear to arise due to autonomic variability.
Although the interval-based variability technique of Andersson and the individual feature-based technique of Bharmi et al. are both effective, it would be desirable to provide additional or alternative IEGM-based techniques for tracking respiration and it is to that end that the present invention is primarily directed. It is also desirable to provide techniques of detecting episodes of abnormal respiration from IEGM signals and other aspects of the invention are directed to that end as well.